Formation of the three primary germ layers during gastrulation is an essential step in the establishment of the vertebrate body plan and is associated with major transcriptional changes [1][2][3][4][5] . Global epigenetic reprogramming accompanies these changes [6][7][8] , but the role of the epigenome in regulating early cell fate choice remains unresolved, and the coordination between different molecular layers is unclear. Here we describe the first single cell triple-omics map of chromatin accessibility, DNA methylation and RNA expression during the onset of gastrulation in mouse embryos. The initial exit from pluripotency coincides with the establishment of a global repressive epigenetic landscape, followed by the emergence of lineage-specific epigenetic patterns during gastrulation. Notably, cells committed to mesoderm and endoderm undergo widespread coordinated epigenetic rearrangements at enhancer marks, driven by TET-mediated demethylation, and a concomitant increase of accessibility. In striking contrast, the methylation and accessibility landscape of ectodermal cells is already established in the early epiblast. Hence, regulatory elements associated with each germ layer are either epigenetically primed or remodelled prior to cell fate decisions, providing the molecular logic for a hierarchical emergence of the primary germ layers.Recent technological advances have enabled the profiling of multiple molecular layers at single cell resolution 9-13 , providing novel opportunities to study the relationship between the transcriptome and epigenome during cell fate decisions. We applied scNMT-seq (singlecell Nucleosome, Methylome and Transcriptome sequencing 12 ) to profile 1,105 single cells isolated from mouse embryos at four developmental stages (Embryonic Day (E) 4.5, E5.5, E6.5 and E7.5) which comprise the exit from pluripotency and primary germ layer specification (Figure 1a-d, Extended Data Fig. 1). Cells were assigned to a specific lineage by mapping their RNA expression profiles to a comprehensive single-cell atlas 4 from the same stages, when available, or using marker genes (Extended Data Fig. 2). By performing Argelaguet et al.

Parallel single-cell sequencing protocols represent powerful methods for investigating regulatory relationships, including epigenome-transcriptome interactions. Here, we report a single-cell method for parallel chromatin accessibility, DNA methylation and transcriptome profiling. scNMT-seq (single-cell nucleosome, methylation and transcription sequencing) uses a GpC methyltransferase to label open chromatin followed by bisulfite and RNA sequencing. We validate scNMT-seq by applying it to differentiating mouse embryonic stem cells, finding links between all three molecular layers and revealing dynamic coupling between epigenomic layers during differentiation.

Summary Glioblastoma multiforme (GBM) is an aggressive brain tumor for which current immunotherapy approaches have been unsuccessful. Here, we explore the mechanisms underlying immune evasion in GBM. By serially transplanting GBM stem cells (GSCs) into immunocompetent hosts, we uncover an acquired capability of GSCs to escape immune clearance by establishing an enhanced immunosuppressive tumor microenvironment. Mechanistically, this is not elicited via genetic selection of tumor subclones, but through an epigenetic immunoediting process wherein stable transcriptional and epigenetic changes in GSCs are enforced following immune attack. These changes launch a myeloid-affiliated transcriptional program, which leads to increased recruitment of tumor-associated macrophages. Furthermore, we identify similar epigenetic and transcriptional signatures in human mesenchymal subtype GSCs. We conclude that epigenetic immunoediting may drive an acquired immune evasion program in the most aggressive mesenchymal GBM subtype by reshaping the tumor immune microenvironment.

Parallel single-cell sequencing protocols represent powerful methods for investigating regulatory relationships, including epigenome-transcriptome interactions. Here, we report a novel single-cell method for parallel chromatin accessibility, DNA methylation and transcriptome profiling. scNMT-seq (single-cell nucleosome, methylation and transcription sequencing) uses a GpC methyltransferase to label open chromatin followed by bisulfite and RNA sequencing. We validate scNMT-seq by applying it to differentiating mouse embryonic stem cells, finding links between all three molecular layers and revealing dynamic coupling between epigenomic layers during differentiation.

Measurements of single-cell methylation are revolutionizing our understanding of epigenetic control of gene expression, yet the intrinsic data sparsity limits the scope for quantitative analysis of such data. Here, we introduce Melissa (MEthyLation Inference for Single cell Analysis), a Bayesian hierarchical method to cluster cells based on local methylation patterns, discovering patterns of epigenetic variability between cells. The clustering also acts as an effective regularization for data imputation on unassayed CpG sites, enabling transfer of information between individual cells. We show both on simulated and real data sets that Melissa provides accurate and biologically meaningful clusterings and state-of-the-art imputation performance. Electronic supplementary material The online version of this article (10.1186/s13059-019-1665-8) contains supplementary material, which is available to authorized users.

Supplementary data are available at Bioinformatics online.

High-throughput single-cell measurements of DNA methylomes can quantify methylation heterogeneity and uncover its role in gene regulation. However, technical limitations and sparse coverage can preclude this task. scMET is a hierarchical Bayesian model which overcomes sparsity, sharing information across cells and genomic features to robustly quantify genuine biological heterogeneity. scMET can identify highly variable features that drive epigenetic heterogeneity, and perform differential methylation and variability analyses. We illustrate how scMET facilitates the characterization of epigenetically distinct cell populations and how it enables the formulation of novel hypotheses on the epigenetic regulation of gene expression. scMET is available at https://github.com/andreaskapou/scMET.

Measurements of DNA methylation at the single cell level are promising to revolutionise our understanding of epigenetic control of gene expression. Yet, intrinsic limitations of the technology result in very sparse coverage of CpG sites (around 5% to 20% coverage), effectively limiting the analysis repertoire to a semi-quantitative level. Here we introduce Melissa (MEthyLation Inference for Single cell Analysis), a Bayesian hierarchical method to quantify spatially-varying methylation profiles across genomic regions from single-cell bisulfite sequencing data (scBS-seq). Melissa clusters individual cells based on local methylation patterns, enabling the discovery of epigenetic differences and similarities among individual cells. The clustering also acts as an effective regularisation method for imputation of methylation on unassayed CpG sites, enabling transfer of information between individual cells. We show both on simulated and real data sets that Melissa provides accurate and biologically meaningful clusterings, and state-of-the-art imputation performance. An R implementation of Melissa is publicly available at https://github.com/andreaskapou/Melissa.